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AI Engineer with less than a year in NLP, Computer Vision, and RAG systems.
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AI Engineer with hands-on experience building and deploying production-grade systems across NLP, Computer Vision, and RAG including a multilingual AI-text detector achieving 98.4% F1 on 1M samples and a full-stack RAG API supporting multi-provider LLM and vector search backends. Proficient in the full ML lifecycle from data engineering and model fine-tuning to FastAPI deployment and Docker containerization.
October 6 University
B.Sc. · Computer Science & Information Systems (AI Track)
August 1, 2023 – June 30, 2026
National Telecommunication Institute (NTI)
Huawei AI & Data Science Program
April 1, 2025 – July 1, 2025
Giza, Giza Governorate, Egypt
Digital Egypt Pioneers Initiative (DEPI), ITIDA
AI & Machine Learning Engineer
April 1, 2024 – October 1, 2024
Giza, Giza Governorate, Egypt
Mini RAG App Retrieval-Augmented Generation API
March 1, 2026 – Present
Built a full-stack RAG pipeline as a FastAPI REST API supporting document upload, chunking, vector indexing, semantic search, and LLM-powered answer generation over user-uploaded PDFs and text files. Designed a modular multi-provider architecture using Factory & Interface patterns for both LLM (OpenAI, Cohere) and Vector DB (Qdrant) backends, enabling zero-code provider swapping via environment config. Implemented an async data pipeline: file validation & upload → LangChain chunking with configurable size/overlap → embedding generation → batch upsert into Qdrant vector store, with paginated indexing for large corpora. Integrated MongoDB (via Motor async driver) for metadata management with Pydantic schema validation and Docker Compose containerization; supported multi-language prompt templates (EN/AR) for flexible RAG generation.
View ProjectFake Account Detection (Twitter) End-to-End ML + Deployment
January 1, 2026 – Present
Built an end-to-end system to classify Twitter accounts as fake/bot vs real with confidence scoring, covering preprocessing, feature engineering, modeling, and deployment. Engineered 29 features across behavioral, linguistic, and account metadata dimensions; tuned decision threshold to 0.445 and ran GridSearchCV (180 fits, ROC-AUC scoring) for optimal hyperparameter selection. Achieved 92% accuracy, 92% precision, 92% recall, and 98% ROC-AUC; deployed as a FastAPI REST API + Streamlit UI, containerized with Docker, with automated tests and CI/CD. Integrated AWS S3 in production to store and retrieve model artifacts reliably for scalable deployments.
View ProjectAI-Generated Text Detection Multilingual Binary Classifier
January 1, 2025 – Present
Built a multilingual AI-generated text detector supporting Arabic & English using XLM-RoBERTa-base fine-tuned on a combined 1M sample dataset (RAID for English, ALHD for Arabic), achieving 98.41% validation F1. Engineered a custom PyTorch training loop with AdamW, AMP fp16 mixed-precision, gradient accumulation, and DataParallel across 2×T4 GPUs on Kaggle for memory-efficient large-scale fine-tuning. Designed a scalable data pipeline: cleaned and filtered the RAID dataset (removing adversarial homoglyph samples), chunked Wikipedia articles for balance, and merged with the ALHD Arabic corpus into a 1M balanced binary classification dataset. Deployed a FastAPI inference service exposing a REST endpoint returning predicted label + confidence score; planned a fact-checking extension via Google Custom Search API + Gemini API for source verification.
View ProjectBrain Tumor Segmentation Deep Learning + Deployment
January 1, 2025 – Present
Built an end-to-end semantic segmentation system to detect and segment brain tumors from MRI scans, covering COCO annotation parsing, mask generation, model training, evaluation, and interactive deployment. Trained on 2,146 MRI images (1,502 train / 429 valid / 215 test) with COCO-format annotations; implemented custom data generators for memory-efficient batch-by-batch loading and data augmentation (flips, rotations). Designed a U-Net decoder with EfficientNetB3 (ImageNet) encoder and skip connections; used a combined loss (Weighted CE + Dice + Focal) to handle severe class imbalance; enabled Mixed Precision (FP16) for 2x faster training and 50% less VRAM. Achieved 98.2% pixel accuracy, 75.3% Mean IoU, and 76.7% best validation IoU; deployed as an interactive Streamlit web app with real-time inference, adjustable overlay, per-class statistics, and downloadable results.
View ProjectGenerative AI
NVIDIA DLI
September 1, 2025 – Present
Artificial Intelligence (AI)
NTI/Huawei Egyptian Talent Academy
April 1, 2025 – Present
Microsoft Machine Learning Engineer
Digital Egypt Pioneers Initiative (DEPI)
April 1, 2024 – Present
Cultural Fit Analysis
The candidate's project diversity, covering NLP (RAG, text detection), Computer Vision (brain tumor segmentation, image captioning), and traditional ML (fake account detection), indicates a broad interest and adaptability, which aligns well with dynamic AI roles. Their involvement in initiatives like DEPI and NTI/Huawei programs, along with multiple certifications, shows a proactive learning attitude and commitment to professional development. The focus on end-to-end deployment and MLOps in personal projects suggests a practical, results-oriented mindset.
Soft Skills & Operational Fit
The candidate demonstrates strong problem-solving skills through optimizing chunking parameters and conducting qualitative error analysis. Their project descriptions highlight a structured approach to development, including modular architecture design and clean code principles. Experience in team-based projects (DEPI internship) indicates an ability to collaborate effectively. The detailed project descriptions suggest good communication of technical concepts.